Hyperspectral Estimation of Apple Canopy SPAD Values Based on Optimized Spectral Indices and CEO-LSSVM

Leaf chlorophyll content (LCC) is a key physiological parameter affecting plant growth and development. Rapid and non-destructive monitoring of LCC using hyperspectral remote sensing is crucial for promoting precision agriculture. In this study, hyperspectral data of apple canopy leaves at different phenological stages were collected alongside their corresponding SPAD values (representing LCC) to construct a dataset. Two types of spectral features were extracted: (1) optimized spectral index combinations; and (2) feature bands selected using the Successive Projections Algorithm (SPA). Based on these features, three machine learning models—Support Vector Machine (SVM), Least Squares Support Vector Machine (LSSVM), and Chaos Evolution Optimization-enhanced LSSVM (CEO-LSSVM)—were developed to estimate SPAD values. The results indicate that the constructed optimal spectral index combinations exhibit superior sensitivity in SPAD estimation compared to the feature bands selected by SPA. Specifically, during the physiological fruit drop stage, the CEO-LSSVM model based on spectral indices achieved a test set R2 of 0.851, surpassing the SPA-based model (R2 = 0.813). Regarding model performance, the CEO-LSSVM demonstrated the highest accuracy and robustness across all stages. In the fruit drop period, using optimized spectral indices, it achieved an RMSE of 1.338, significantly outperforming the LSSVM (RMSE = 1.703) and SVM (RMSE = 2.409) models. This superiority was further evident in the fruit enlargement stage, where the CEO-LSSVM model reached a peak test set R2 of 0.868 and the lowest RMSE of 1.254. The integrated model combining optimized spectral indices and CEO-LSSVM provides an efficient and high-precision approach for hyperspectral SPAD estimation in apple canopies, effectively addressing the challenges of inversion modeling in arid oasis environments.